Question 1:
We will use the codes that we’ve learnt in the class for this question. We will first use the gutenbergr package to find our 15 books. then we will lower the cases and count the frequency of the words.
But first we will declare our needed libraries.
#Required Packages:
library(ggplot2)
library(stringr)
library(wordcloud)
library(tm)
library(gutenbergr)
library(tidytext)
library(gsubfn)
library(wordcloud2)
library(ngram)
library(dplyr)
library(highcharter)
library(SnowballC)
library(RColorBrewer)Now we will run our code to find the mose frequent words in Charles Dickens works:
#HW8-Q1
#---------------------------
#Finding all the books of Charles Dickens:
gutenberg_works(author == "Dickens, Charles")## # A tibble: 74 x 8
## gutenberg_id title author gutenberg_autho~ language gutenberg_books~
## <int> <chr> <chr> <int> <chr> <chr>
## 1 46 A Chris~ Dicke~ 37 en Christmas/Child~
## 2 98 A Tale ~ Dicke~ 37 en Historical Fict~
## 3 564 The Mys~ Dicke~ 37 en Mystery Fiction
## 4 580 The Pic~ Dicke~ 37 en Best Books Ever~
## 5 588 Master ~ Dicke~ 37 en <NA>
## 6 644 The Hau~ Dicke~ 37 en Christmas
## 7 650 Picture~ Dicke~ 37 en <NA>
## 8 653 "The Ch~ Dicke~ 37 en <NA>
## 9 675 America~ Dicke~ 37 en <NA>
## 10 678 The Cri~ Dicke~ 37 en Children's Lite~
## # ... with 64 more rows, and 2 more variables: rights <chr>,
## # has_text <lgl>#---------------------------
#List of books and their names:
books_list =data.frame(ID= c(580,
730,
967,
700,
917,
968,
821,
766,
1023,
786,
963,
98,
1400,
883,
564) ,Name=
c( "ThePickwickPapers","OliverTwist",
"NicholasNickleby", "TheOldCuriosityShop","BarnabyRudge","MartinChuzzlewit","DombeyandSon",
"DavidCopperfield",
"BleakHouse",
"HardTimes",
"LittleDorrit",
"ATaleofTwoCities",
"GreatExpectations",
"OurMutualFriend",
"TheMysteryofEdwinDrood"))
#---------------------------
# Using the function that we used in class
# This function will fitler the stop words and split the books:
words <- function(i,k)
{
x = gutenberg_download(k)
# removing the punctuations:
x = x %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]","") %>%
str_split(pattern = "\\s") %>%
unlist() %>%
table() %>%
as.data.frame(stringsAsFactors = F)
colnames(x) = c("word","count")
x = x %>%
filter(!str_to_lower(word) %in% stop_words$word) %>% #filtering stop words
filter(str_length(word)>1) %>%
filter(!str_detect(word,"\\d")) %>%
mutate(Book = books_list[i,2]) %>%
arrange(desc(count)) %>%
mutate(proper = !word %in% str_to_lower(word))
dickens_list = x
return(dickens_list)
}
#---------------------------
#Binding the books:
n = length(books_list$ID)
out = list()
for( i in 1:n)
{
out[[i]] = words(i,books_list[i,1])
}
dickens = bind_rows(out)
dickens$word <- tolower(dickens$word)
#---------------------------
#Sorting:
dickens %>%
group_by(word) %>%
summarise(total_count = sum(count)) %>%
arrange(desc(total_count))-> dickens_sorted
dickens_sorted = dickens_sorted[-c(4),]
#---------------------------
#Plotting the most used words:
dickens_sorted[1:10,] %>%
hchart("column",hcaes(x = word, y = total_count)) %>%
hc_add_theme(hc_theme_ffx()) %>%
hc_yAxis(title =list(text = "Total Number of Repitions"))#---------------------------Question 2:
We will use the wordcloud2 package. From the last question we found the top frequent words which are not in the stop_words. We will use the given image in the home work to plot the wordcloud of the top 200 most frequent words.
#HW8-Q2:
#---------------------------
#Sorting:
dickens_sorted %>%
select(word = word, freq = total_count) -> dickens_sorted_plot
#---------------------------
#wordcloud2 library(we will download it again to ensure it plots in rmarkdown)
library(devtools)
devtools::install_github("lchiffon/wordcloud2")
library(wordcloud2)
#---------------------------
#Plotting All of them:
wordcloud2(dickens_sorted_plot,size = 0.2,
figPath = "/Users/shervin/Desktop/untitled folder 3/Unknown.png")#---------------------------
# The first 200:
wordworth = dickens_sorted_plot[1:200,]
wordcloud2(wordworth,size = 0.1,
figPath = "/Users/shervin/Desktop/untitled folder 3/Unknown.png")Question 3:
In this question we will find the main characters by finding all the upper case words and checking whether in the books exists any lower case of these words. If the lower case also exists therefore it is not a character and thus we can exclude them. The remaining words are the characters forename and surname. As we can’t have a specific way to differentiate if it is the surname fo forename therefore we may exclude the assumption that we are looking only for the name.Therefore in this question we found the names and the surnames.
#HW8-3:
#---------------------------
#Declaring a function which will find the main characters:
main_char <- function(i){
value = as.data.frame(out[i]) # "out" from the first question: words of every book
unique = unique(value)
unique_lower = unique[grep("^[a-z]", unique$word),] # all the lower case unique words
temp = value[grep("^[A-Z]", value$word),]
temp$word <- tolower(temp$word)
index =which(temp$word =="sir")
if(length(index)!=0)
temp = temp[-index,]
for (k in 1:100)
{
if(temp$word[k] %in% unique_lower$word )
temp = temp[-k,]
}
return(temp$word[1:5])
}
#---------------------------
#Finding the main characters of the 15 books:
output = list()
for ( i in 1:15)
{
output[[i]] = main_char(i)
}
main_characters = output
main_characters2 <- sapply(main_characters, tolower)
#---------------------------
#Finding the number of times the names have come in the books:
dickens %>%
filter(word %in% main_characters2 ) %>%
group_by(Book) %>%
mutate(percent = round(100*count/sum(count))) %>%
hchart("column",hcaes(x = Book, y = percent, group = word)) %>%
hc_add_theme(hc_theme_ffx())#---------------------------Question 4:
We will use the sentiments package to find the necessary words.
#HW8-Q4:
a = list()
for (i in 1:15)
{
book = out[[i]]
book$word <- tolower(book$word)
book %>% group_by(word) %>%
summarise(count = sum(count)) %>%
arrange(desc(count)) -> book_count
book_count %>%
mutate( is.sentiment = (word %in% sentiments$word) ) -> book_sentiment
book_sentiment %>%
filter(is.sentiment == TRUE) -> book_sentiment
row_sentiment = length(book_sentiment$word)
for( j in 1:row_sentiment)
{
book_sentiment$type[j] = sentiments$sentiment[which(
sentiments$word==book_sentiment$word[j])]
}
book_sentiment = na.omit(book_sentiment)
book_sentiment %>%
select(word,count,type) %>%
filter(type == "positive") -> book_sentiment_p
book_sentiment %>%
select(word,count,type) %>%
filter(type == "negative") -> book_sentiment_n
binding = rbind(book_sentiment_p[1:20,],book_sentiment_n[1:20,])
layout(matrix(c(1, 2), nrow=2), heights=c(2, 3))
par(mar=rep(0, 4))
plot.new()
text(x=0.5, y=0.01, sprintf("Negative vs Positive of the book %s",books_list[i,2]))
wordcloud(binding$word,binding$count,
random.order=FALSE, rot.per=0.1,
ordered.colors=TRUE,
colors=brewer.pal(3, "Dark2")[factor(binding$type)],
main="Title",
scale=c(8,.3))
name = factor(factor(binding$type),levels(factor(binding$type))[c(2,1)])
legend("topright",
legend = levels(factor(binding$type)),
text.col=brewer.pal(8, "Dark2")[unique(name)])
book_sentiment %>%
select(word,count,type) %>%
filter( !(type %in% c("negative","positive"))) -> book_sentiment_feel
hc_sentiment <- highchart() %>%
hc_add_series(name = "frequency of two words",data = book_sentiment_feel[1:8,],
type = "bar",
mapping = hcaes(x = word, y = count),
colorByPoint = TRUE) %>%
hc_xAxis(title = list(text = "Name of the Words"),
categories = book_sentiment_feel$word) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text =sprintf("Sentiment in %s",books_list[i,2]))
a[[i]] = hc_sentiment
}
htmltools::tagList(a)Qustion 5:
In this question question we will define some function to find the negative and positive sentiments and divide the Les Miserables to 200 pieces.
#HW8 -Q5:
#---------------------------
# Fundning to sentiments of negative and positive:
sentiments %>%
filter(sentiment ==c("negative")) -> negative_sentiments #negative
sentiments %>%
filter(sentiment ==c("positive")) -> positive_sentiments #positive
#---------------------------
#Finding works of Victor Hugo:
Hugo = gutenberg_works(author == "Hugo, Victor")
words <- function(i,k)
{
x = gutenberg_download(k)
x = x %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]","") %>%
str_split(pattern = "\\s") %>%
unlist() %>%
table() %>%
as.data.frame(stringsAsFactors = F)
colnames(x) = c("word","count")
x = x %>%
filter(!str_to_lower(word) %in% stop_words$word) %>%
filter(str_length(word)>1) %>%
filter(!str_detect(word,"\\d")) %>%
mutate(Book = books_list[i,2]) %>%
arrange(desc(count)) %>%
mutate(proper = !word %in% str_to_lower(word))
dickens_list = x
return(dickens_list)
}
#Listing the 5 books of Les Miserables:
hugo_books_list = c(48731:48735)
m = length(hugo_books_list)
check = list()
for( i in 1:m)
{
check[[i]] = words(i,hugo_books_list[i])
}
books_split_hugo = list()
for (i in 1:m)
{
h = gutenberg_download(hugo_books_list[i])
books_split_hugo[[i]] =str_split(h[,2],boundary("word"))
}
books_split_hugo_unlist = unlist(books_split_hugo)
#---------------------------
#Deviding the whole book to 200 pieces:
sample = list()
g = length(books_split_hugo_unlist)
for(i in 1:200 )
{
sample[[i]] = books_split_hugo_unlist[1+ceiling((i-1)*(g/200)):ceiling((i*g/200))]
}
#---------------------------
#Function which will find the number of positive sentiments:
sentiments_positive <- function(i)
{
sample_data = as.data.frame(sample[[i]])
sample_data_words = sample[[i]]
sample_data <- sapply(sample_data,tolower)
sample_data_words = sample_data_words %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]","") %>%
str_split(pattern = "\\s") %>%
unlist() %>%
table() %>%
as.data.frame(stringsAsFactors = F)
colnames(sample_data_words) = c("word","count")
sample_data_words = sample_data_words %>%
filter(!str_to_lower(word) %in% stop_words$word) %>%
filter(str_length(word)>1) %>%
filter(!str_detect(word,"\\d")) %>%
arrange(desc(count)) %>%
mutate(proper = !word %in% str_to_lower(word))
hugo = sample_data_words
hugo %>%
mutate(is.negative = (hugo$word %in% negative_sentiments$word)) -> hugo_negative
hugo_negative %>%
group_by(is.negative) %>%
summarise(count = sum(count)) -> hugo_negative_count
hugo %>%
mutate(is.positive = (hugo$word %in% positive_sentiments$word)) -> hugo_positive
hugo_positive %>%
group_by(is.positive) %>%
summarise(count = sum(count)) -> hugo_positive_count
return(hugo_positive_count$count[2])
}
#---------------------------
#Function which will find the number of negative sentiments:
sentiments_negative <- function(i)
{
sample_data = as.data.frame(sample[[i]])
sample_data_words = sample[[i]]
sample_data <- sapply(sample_data,tolower)
sample_data_words = sample_data_words %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]","") %>%
str_split(pattern = "\\s") %>%
unlist() %>%
table() %>%
as.data.frame(stringsAsFactors = F)
colnames(sample_data_words) = c("word","count")
sample_data_words = sample_data_words %>%
filter(!str_to_lower(word) %in% stop_words$word) %>%
filter(str_length(word)>1) %>%
filter(!str_detect(word,"\\d")) %>%
arrange(desc(count)) %>%
mutate(proper = !word %in% str_to_lower(word))
hugo = sample_data_words
hugo %>%
mutate(is.negative = (hugo$word %in% negative_sentiments$word)) -> hugo_negative
hugo_negative %>%
group_by(is.negative) %>%
summarise(count = sum(count)) -> hugo_negative_count
return(hugo_negative_count$count[2])
}
#---------------------------
#Finding the number of Positive and Negatives:
positive = c(1:200)
negative = c(1:200)
for (i in 1:200)
{
positive[i] = sentiments_positive(i)
negative[i] = sentiments_negative(i)
}
#---------------------------
#Plotting the values:
plot(negative,type="line", xlab = "Piece Number", ylab = "Frequency", main="Neagtive words")
plot(positive, type= "line",xlab = "Piece Number", ylab = "Frequency", main ="Positive")
hc <- highchart() %>%
hc_add_series(name ="Negative",
type ="area",
data =negative) %>%
hc_add_series(name = "Positive",
type = "area",
data = positive) %>%
hc_xAxis(title = list(text = "Piece Number")) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text =sprintf("Positive-Negative in Les Miserables divided in 200 pieces"))
hc#---------------------------Question 6:
We will find the two words using the ngram package. With this package we can find every n consecutive words.
#HW8-Q6:
#---------------------------
#We will first extract the books:
hug = gutenberg_download(48731:48735)
#---------------------------
#Now we will remove the uncessary punctuations and give a sign for the places
#that have ", . ! @" this sign is "!!!!!" we will need this sign:
hugs = hug %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]"," \\!!!!!")
hugs <- sapply(hugs,tolower)
#---------------------------
#Using the N-grqm package we will find the 2 word:
ng <- ngram(hugs,n=2) #ng object
ngram = get.phrasetable(ng)
ngrams = as.data.frame(ngram) #making it as a data frame
#---------------------------
# length or ngram:
J = length(ngrams$ngrams)
#---------------------------
# we will find those that have punctuations using the statment
# we said in the upper lines:
check_ngram = str_extract(ngrams$ngrams,"\\!") # removing punctuations
indexes = which(check_ngram==c("!")) # indexes of those we dont want
#---------------------------
# Removed indesxes:
ngrams2 = ngram[-indexes,]
ngrams2$ngrams = sapply(ngrams2$ngrams,tolower)
#---------------------------
# Now we will remove the stop words:
for(k in 1:50)
{
for ( i in 1:100)
{
tempe =ngrams2$ngrams[i]
tempe_split = str_split(tempe," ")
tempe_split = unlist(tempe_split)
if(sum(tempe_split %in% stop_words$word))
ngrams2 = ngrams2[-i,]
}
}
#----------------------------------------
#Plotting the most frequent 30 words:
ngrams2 = as.data.frame(ngrams2)
hc_two_words <- highchart() %>%
hc_add_series(name = "frequency of two words",data = ngrams2[1:30,],
type = "column",
mapping = hcaes(x = ngrams, y = freq),
color = "navy blue") %>%
hc_xAxis(title = list(text = "Name of the Two Words"),
categories = ngrams2$ngrams) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text ="Frequency of two words in Les Miserables")
hc_two_words #----------------------------------------Question 7:
We use the ngram package and the result of the last question to find the words that start with he and she and with these combinationats we will find the most used verbs.
#HW8-Q7:
#---------------------------
#We will first extract the books:
dickens_charles = gutenberg_download(books_list[,1])
#---------------------------
#Now we will remove the uncessary punctuations and give a sign for the places
#that have ", . ! @" this sign is "!!!!!" we will need this sign:
dickens_charles_str = dickens_charles %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]"," \\!!!!!")
dickens_charles_str <- sapply(dickens_charles_str,tolower)
#---------------------------
#Using the N-grqm package we will find the 2 word:
ng <- ngram(dickens_charles_str,n=2) #ng object
ngram = get.phrasetable(ng)
ngrams = as.data.frame(ngram) #making it as a data frame
#---------------------------
# length or ngram:
J = length(ngrams$ngrams)
#---------------------------
# we will find those that have punctuations using the statment
# we said in the upper lines:
check_ngram = str_extract(ngrams$ngrams,"\\!") # removing punctuations
indexes = which(check_ngram==c("!")) # indexes of those we dont want
#---------------------------
# Removed indesxes:
ngrams_dickens = ngram[-indexes,]
J2 = length(ngrams_dickens$ngrams)
ngrams_dickens$ngrams = sapply(ngrams_dickens$ngrams,tolower)
#---------------------------
# Now we will find the words that start with he/she:
rows_dickens = nrow(ngrams_dickens)
index_dickens = list()
b = 1
for(i in 1:rows_dickens)
{
tempe =ngrams_dickens$ngrams[i]
tempe_split = str_split(tempe," ")
tempe_split = unlist(tempe_split)
if(sum(tempe_split[1] %in% c("he","she")) )
{
index_dickens[b] = i
b = b+1
}
}
#---------------------------
# Those that start with he or she:
index_dickens = unlist(index_dickens)
ngrams_dickens_he_she = ngrams_dickens[index_dickens,]
ngrams_dickens_he_she## ngrams freq prop
## 82 he had 5724 9.517839e-04
## 94 he was 4679 7.780218e-04
## 205 she was 2102 3.495195e-04
## 210 she had 2056 3.418707e-04
## 292 he said 1498 2.490867e-04
## 295 he is 1486 2.470914e-04
## 312 he would 1402 2.331239e-04
## 358 he could 1228 2.041912e-04
## 539 he has 868 1.443306e-04
## 789 she would 623 1.035921e-04
## 790 she said 623 1.035921e-04
## 859 he might 577 9.594328e-05
## 869 he did 573 9.527816e-05
## 872 she is 571 9.494560e-05
## 959 he looked 524 8.713046e-05
## 964 he were 522 8.679791e-05
## 1051 he took 474 7.881649e-05
## 1060 he went 470 7.815137e-05
## 1098 she could 454 7.549090e-05
## 1150 he should 430 7.150019e-05
## 1420 he came 358 5.952807e-05
## 1445 she has 354 5.886295e-05
## 1451 he knew 353 5.869667e-05
## 1466 he saw 349 5.803155e-05
## 1587 he made 322 5.354200e-05
## 1601 he thought 319 5.304316e-05
## 1709 he sat 300 4.988385e-05
## 1754 he will 291 4.838734e-05
## 1755 he must 291 4.838734e-05
## 1811 he spoke 284 4.722338e-05
## 1918 he felt 270 4.489547e-05
## 1935 he never 267 4.439663e-05
## 1939 she did 267 4.439663e-05
## 1953 he turned 265 4.406407e-05
## 1991 he stood 260 4.323267e-05
## 2089 she say 248 4.123732e-05
## 2124 he returned 244 4.057220e-05
## 2366 he found 220 3.658149e-05
## 2380 she looked 219 3.641521e-05
## 2454 he added 212 3.525126e-05
## 2464 she might 211 3.508498e-05
## 2545 he may 205 3.408730e-05
## 2577 he seemed 203 3.375474e-05
## 2628 she were 198 3.292334e-05
## 2790 he put 188 3.126055e-05
## 2852 he can 184 3.059543e-05
## 2928 he got 180 2.993031e-05
## 3106 she took 170 2.826752e-05
## 3167 he and 167 2.776868e-05
## 3205 he gave 165 2.743612e-05
## 3226 he began 164 2.726984e-05
## 3269 she sat 162 2.693728e-05
## 3296 she went 160 2.660472e-05
## 3384 he asked 156 2.593960e-05
## 3409 she saw 155 2.577332e-05
## 3419 she should 154 2.560704e-05
## 3524 he stopped 150 2.494193e-05
## 3666 he didn 145 2.411053e-05
## 3710 he answered 143 2.377797e-05
## 3714 he replied 143 2.377797e-05
## 3794 she came 140 2.327913e-05
## 3798 she answered 140 2.327913e-05
## 3840 she knew 138 2.294657e-05
## 3869 he told 137 2.278029e-05
## 3877 he heard 137 2.278029e-05
## 3926 he walked 135 2.244773e-05
## 3929 he does 135 2.244773e-05
## 3934 he knows 135 2.244773e-05
## 4086 he then 130 2.161634e-05
## 4290 she returned 123 2.045238e-05
## 4319 he held 123 2.045238e-05
## 4344 he left 122 2.028610e-05
## 4462 she thought 119 1.978726e-05
## 4537 he comes 117 1.945470e-05
## 4541 she will 117 1.945470e-05
## 4661 she never 114 1.895586e-05
## 4696 she made 114 1.895586e-05
## 4922 he fell 109 1.812447e-05
## 5069 he passed 106 1.762563e-05
## 5148 he puts 104 1.729307e-05
## 5184 he drew 103 1.712679e-05
## 5308 he couldn 101 1.679423e-05
## 5330 he wanted 101 1.679423e-05
## 5442 he meant 98 1.629539e-05
## 5463 she must 98 1.629539e-05
## 5575 she gave 96 1.596283e-05
## 5633 he don 95 1.579655e-05
## 5735 he cried 93 1.546399e-05
## 5815 she felt 91 1.513144e-05
## 5921 she spoke 90 1.496516e-05
## 5932 he always 90 1.496516e-05
## 5939 he shook 90 1.496516e-05
## 5964 he now 89 1.479888e-05
## 5982 she turned 89 1.479888e-05
## 5990 she asked 89 1.479888e-05
## 6002 he wore 89 1.479888e-05
## 6008 he called 89 1.479888e-05
## 6151 she seemed 87 1.446632e-05
## 6189 he laid 86 1.430004e-05
## 6220 she stood 86 1.430004e-05
## 6317 he wouldn 84 1.396748e-05
## 6474 he became 82 1.363492e-05
## 6567 he ever 81 1.346864e-05
## 6692 she and 79 1.313608e-05
## 6694 he appeared 79 1.313608e-05
## 6804 he used 78 1.296980e-05
## 6921 he only 77 1.280352e-05
## 6969 she replied 76 1.263724e-05
## 7001 she says 76 1.263724e-05
## 7087 he still 75 1.247096e-05
## 7162 he kept 74 1.230468e-05
## 7421 he really 72 1.197212e-05
## 7423 he raised 72 1.197212e-05
## 7458 she may 71 1.180585e-05
## 7610 he goes 70 1.163957e-05
## 7616 he lay 70 1.163957e-05
## 7830 she added 68 1.130701e-05
## 7881 he rose 67 1.114073e-05
## 7897 he observed 67 1.114073e-05
## 7958 he looks 67 1.114073e-05
## 8026 he not 66 1.097445e-05
## 8155 she can 65 1.080817e-05
## 8201 he soon 65 1.080817e-05
## 8342 he repeated 64 1.064189e-05
## 8344 she cried 64 1.064189e-05
## 8392 he carried 63 1.047561e-05
## 8488 she stopped 63 1.047561e-05
## 8517 she does 62 1.030933e-05
## 8575 he hadn 62 1.030933e-05
## 8590 he shall 62 1.030933e-05
## 8773 he takes 61 1.014305e-05
## 8833 he won 60 9.976771e-06
## 8850 she held 60 9.976771e-06
## 9090 he lived 58 9.644212e-06
## 9165 she think 58 9.644212e-06
## 9424 he remained 56 9.311653e-06
## 9425 he wants 56 9.311653e-06
## 9528 she got 56 9.311653e-06
## 9617 he resumed 55 9.145373e-06
## 9725 he paused 55 9.145373e-06
## 9747 he set 54 8.979094e-06
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## 630697 she lying 1 1.662795e-07
## 631098 he shielded 1 1.662795e-07
## 631163 she haunts 1 1.662795e-07
## 631991 he subsisted 1 1.662795e-07
## 632607 she knelt 1 1.662795e-07
## 632734 she furled 1 1.662795e-07
## 632964 he woos 1 1.662795e-07
## 632971 she wins 1 1.662795e-07
## 633233 he treat 1 1.662795e-07
## 633725 he thirst 1 1.662795e-07
## 633906 she hesitates 1 1.662795e-07
## 634091 he outraged 1 1.662795e-07
## 634093 she dabbed 1 1.662795e-07
## 634101 he honourably 1 1.662795e-07
## 634129 he afraid 1 1.662795e-07
## 634622 she enough 1 1.662795e-07
## 634888 he confronts 1 1.662795e-07
## 635716 he won<u+2019>t 1 1.662795e-07
## 636064 he conveniently 1 1.662795e-07
## 636167 he viewed 1 1.662795e-07
## 636305 he implored 1 1.662795e-07
## 636783 he brooded 1 1.662795e-07
## 636831 she decamped 1 1.662795e-07
## 638931 he rioted 1 1.662795e-07
## 639096 he announces 1 1.662795e-07
## 639357 he silly 1 1.662795e-07
## 639370 she chronically 1 1.662795e-07
## 639462 she borrowed 1 1.662795e-07
## 639920 he strides 1 1.662795e-07
## 640558 he lie 1 1.662795e-07
## 640628 he von 1 1.662795e-07
## 640942 he delicately 1 1.662795e-07
## 641070 he visited 1 1.662795e-07
## 641144 he reverted 1 1.662795e-07
## 641344 he detects 1 1.662795e-07
## 641500 he resembled 1 1.662795e-07
## 642579 he calmly 1 1.662795e-07
## 642598 he stealeth 1 1.662795e-07
## 642856 he interpolates 1 1.662795e-07
## 642912 he openly 1 1.662795e-07
## 643708 she inevitably 1 1.662795e-07
## 644043 he decides 1 1.662795e-07
## 644278 she scarce 1 1.662795e-07
## 644325 he escape 1 1.662795e-07
## 644581 she conversed 1 1.662795e-07
## 644923 she up 1 1.662795e-07
## 644928 she termed 1 1.662795e-07
## 645026 he flew 1 1.662795e-07
## 645403 he sorry 1 1.662795e-07
## 645613 he anoints 1 1.662795e-07
## 645689 he philosophically 1 1.662795e-07
## 646136 she lightened 1 1.662795e-07
## 646289 she brewed 1 1.662795e-07
## 646529 he been<u+2014>so 1 1.662795e-07
## 646801 he presses 1 1.662795e-07
## 647615 she advances 1 1.662795e-07
## 647808 he benevolently 1 1.662795e-07
## 648090 she stuck 1 1.662795e-07
## 648108 she acquired 1 1.662795e-07
## 648181 she summons 1 1.662795e-07
## 648205 he biting 1 1.662795e-07
## 648392 she designed 1 1.662795e-07
## 648610 he emigrated 1 1.662795e-07
## 648662 he sturdily 1 1.662795e-07
## 648693 he insured 1 1.662795e-07
## 648695 she courted 1 1.662795e-07
## 648815 he corroborated 1 1.662795e-07
## 648830 she flounced 1 1.662795e-07
## 649201 she disclosed 1 1.662795e-07
## 649264 she disgraced 1 1.662795e-07
## 649309 she searched 1 1.662795e-07
## 649432 she sustained 1 1.662795e-07
## 649801 he defers 1 1.662795e-07
## 649996 he deputed 1 1.662795e-07
## 650291 he shared 1 1.662795e-07
## 651214 he consults 1 1.662795e-07
## 651294 he is<u+2014><u+2019> 1 1.662795e-07
## 651590 he drily 1 1.662795e-07
## 651665 he prized 1 1.662795e-07
## 651866 she clapped 1 1.662795e-07
## 652088 she murmured 1 1.662795e-07
## 652233 he hardened 1 1.662795e-07
## 652683 he divests 1 1.662795e-07
## 652787 she starved 1 1.662795e-07
## 652922 he recruiting 1 1.662795e-07
## 652959 he order 1 1.662795e-07
## 653880 she overcame 1 1.662795e-07
## 653912 he thankfully 1 1.662795e-07
## 653998 she cause 1 1.662795e-07
## 654545 she presumes 1 1.662795e-07
## 654689 he inserted 1 1.662795e-07
## 654692 he tarries 1 1.662795e-07
## 654876 she heeded 1 1.662795e-07
## 654912 she try 1 1.662795e-07
## 655062 he gladly 1 1.662795e-07
## 655171 she there 1 1.662795e-07
## 655332 he tangles 1 1.662795e-07
## 655465 he clasps 1 1.662795e-07
## 655654 she associated 1 1.662795e-07
## 655688 he perfects 1 1.662795e-07
## 656085 he reconnoitred 1 1.662795e-07
## 656187 he thawed 1 1.662795e-07
## 656474 he highsted 1 1.662795e-07
## 656892 he stand 1 1.662795e-07
## 656996 he calculated 1 1.662795e-07
## 657111 he unwinds 1 1.662795e-07
## 657132 he crashed 1 1.662795e-07
## 657173 she falters 1 1.662795e-07
## 657331 he incidentally 1 1.662795e-07
## 657752 she shaded 1 1.662795e-07
## 659230 she pretends 1 1.662795e-07
## 659350 she hitched 1 1.662795e-07
## 659653 he mildly 1 1.662795e-07
## 659715 he farther 1 1.662795e-07
## 659921 he signifies 1 1.662795e-07
## 659959 he charges 1 1.662795e-07
## 660673 she owes 1 1.662795e-07
## 661163 she wilfully 1 1.662795e-07
## 661300 she disappears 1 1.662795e-07
## 661584 he shrank 1 1.662795e-07
## 661679 he spared 1 1.662795e-07
## 662911 he hints 1 1.662795e-07
## 662954 she mentally 1 1.662795e-07
## 663299 she asleep 1 1.662795e-07
## 664309 he handsomely 1 1.662795e-07
## 664781 she honoured 1 1.662795e-07
## 665378 she quoted 1 1.662795e-07
## 665643 he departs 1 1.662795e-07
## 665720 he combines 1 1.662795e-07
## 665973 she condemned 1 1.662795e-07
## 666124 he spun 1 1.662795e-07
## 666193 he cutting 1 1.662795e-07
## 666199 she elopes 1 1.662795e-07
## 666548 he swings 1 1.662795e-07
## 666781 he reconciled 1 1.662795e-07
## 667187 he subscribed 1 1.662795e-07
## 667191 he jerk 1 1.662795e-07
## 667361 she squeezed 1 1.662795e-07
## 667480 he differed 1 1.662795e-07
## 667735 she prefers 1 1.662795e-07
## 668068 he interrupt 1 1.662795e-07
## 668371 she concealed 1 1.662795e-07
## 668643 she acquits 1 1.662795e-07
## 668875 he misdoubted 1 1.662795e-07
## 669268 he asleep 1 1.662795e-07
## 669466 he wrested 1 1.662795e-07
## 669485 he rally 1 1.662795e-07
## 669677 he repaid 1 1.662795e-07
## 669847 he stones 1 1.662795e-07
## 669876 he foresee 1 1.662795e-07
## 669900 she extended 1 1.662795e-07
## 670272 he kep 1 1.662795e-07
## 670275 she marshalled 1 1.662795e-07
## 670403 he fades 1 1.662795e-07
## 670490 he love 1 1.662795e-07
## 670727 he spent 1 1.662795e-07
## 671287 she forgives 1 1.662795e-07
## 671635 he stiffly 1 1.662795e-07
## 671646 he complies 1 1.662795e-07
## 671925 she supervised 1 1.662795e-07
## 673273 he behaving 1 1.662795e-07
## 673581 he bespeaks 1 1.662795e-07
## 673763 she declares 1 1.662795e-07
## 673885 he accidentally 1 1.662795e-07
## 673897 he unpacked 1 1.662795e-07
## 674194 she dutifully 1 1.662795e-07
## 674567 she reasoned 1 1.662795e-07
## 675122 he many 1 1.662795e-07
## 675153 he spilled 1 1.662795e-07
## 675275 he exhorts 1 1.662795e-07
## 675492 she freely 1 1.662795e-07
## 675666 he squandered 1 1.662795e-07
## 675678 he personally 1 1.662795e-07
## 675750 he angry 1 1.662795e-07
## 676039 he induced 1 1.662795e-07
## 676199 she kisses 1 1.662795e-07
## 676291 she mashed 1 1.662795e-07
## 676419 she worth 1 1.662795e-07
## 676515 he suspended 1 1.662795e-07
## 676679 he employed 1 1.662795e-07
## 676697 he scrambled 1 1.662795e-07
## 676833 she dragged 1 1.662795e-07
## 677006 he occasioned 1 1.662795e-07
## 677148 she deserved 1 1.662795e-07
## 677776 she finally 1 1.662795e-07
## 677984 she lamented 1 1.662795e-07
## 678116 she rouged 1 1.662795e-07
## 678253 she undrew 1 1.662795e-07
## 678714 she dozing 1 1.662795e-07
## 678940 he grubbed 1 1.662795e-07
## 678973 she replaced 1 1.662795e-07
## 679305 she favored 1 1.662795e-07
## 679408 he amuses 1 1.662795e-07
## 679535 she grins 1 1.662795e-07
## 679854 he perpetrated 1 1.662795e-07
## 679996 she stirred 1 1.662795e-07
## 680176 he existed 1 1.662795e-07
## 680411 she can<u+2019>t 1 1.662795e-07
## 680618 she tires 1 1.662795e-07
## 680638 he bows 1 1.662795e-07
## 680780 she suspects 1 1.662795e-07
## 681708 he halts 1 1.662795e-07
## 681711 she threatens 1 1.662795e-07
## 681730 he frightens 1 1.662795e-07
## 681802 she incoherently 1 1.662795e-07
## 681888 he polished 1 1.662795e-07
## 682469 she slackened 1 1.662795e-07
## 682630 he postponed 1 1.662795e-07
## 682635 he fawned 1 1.662795e-07
## 682654 he such 1 1.662795e-07
## 683080 he straightened 1 1.662795e-07
## 683198 he bawled 1 1.662795e-07
## 683259 he strapped 1 1.662795e-07
## 683307 he known 1 1.662795e-07
## 683330 she replead 1 1.662795e-07
## 683602 he dispenses 1 1.662795e-07
## 683869 he gazes 1 1.662795e-07
## 684278 he scatters 1 1.662795e-07
## 684915 he darkened 1 1.662795e-07
## 685146 he lighting 1 1.662795e-07
## 685397 he enters 1 1.662795e-07
## 685875 he doubtingly 1 1.662795e-07
## 686147 he refrained 1 1.662795e-07
## 686245 he eyes 1 1.662795e-07
## 686312 she bows 1 1.662795e-07
## 686426 he prolonged 1 1.662795e-07
## 686580 he knuckled 1 1.662795e-07
## 686753 she mingled 1 1.662795e-07
## 686926 she contrived 1 1.662795e-07
## 687222 he relents 1 1.662795e-07
## 687291 he outlived 1 1.662795e-07
## 687321 he mad 1 1.662795e-07
## 687414 he overcame 1 1.662795e-07
## 687612 he reproved 1 1.662795e-07
## 687640 she retouches 1 1.662795e-07
## 687713 he mingling 1 1.662795e-07
## 687784 she singing 1 1.662795e-07
## 687965 he disinherited 1 1.662795e-07
## 687974 she charged 1 1.662795e-07
## 687992 he courted 1 1.662795e-07
## 688464 he blinkingly 1 1.662795e-07
## 688715 she fanned 1 1.662795e-07
## 688771 he resting 1 1.662795e-07
## 688862 he soothed 1 1.662795e-07
## 689207 she troubled 1 1.662795e-07
## 689395 he condoled 1 1.662795e-07
## 689464 he names 1 1.662795e-07
## 689492 he bends 1 1.662795e-07
## 689507 he depicts 1 1.662795e-07
## 689670 he condescendingly 1 1.662795e-07
## 689813 he prepares 1 1.662795e-07
## 690173 she condensed 1 1.662795e-07rows_he_she = nrow(ngrams_dickens_he_she)
for( k in 1:10)
{
for ( i in 1:500)
{
tempe =ngrams_dickens_he_she$ngrams[i]
tempe_split = str_split(tempe," ")
tempe_split = unlist(tempe_split)
if(sum(tempe_split[2] %in% stop_words$word))
ngrams_dickens_he_she = ngrams_dickens_he_she[-i,]
}
}
ngrams_dickens_he_she = ngrams_dickens_he_she[-11,]
#---------------------------
# Plotting the verbs with he/she:
hc_he_she <- highchart() %>%
hc_add_series(name = "frequency of verbs",data = ngrams_dickens_he_she[1:30,],
type = "column",
mapping = hcaes(x = ngrams, y = freq),
color = "navy blue") %>%
hc_xAxis(title = list(text = "Name of the Two Words"),
categories = ngrams_dickens_he_she$ngrams) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text ="Frequency of verbs in Les Miserables")
hc_he_she #---------------------------
#Now we will find the verbs:
ngrams_dickens_he_she_new = ngrams_dickens_he_she
verbs_rows = nrow(ngrams_dickens_he_she)
for( i in 1:verbs_rows)
{
tempe =ngrams_dickens_he_she$ngrams[i]
tempe_split = str_split(tempe," ")
tempe_split = unlist(tempe_split)
ngrams_dickens_he_she_new$ngrams[i] = tempe_split[2]
}
ngrams_dickens_he_she_new = as.data.frame(ngrams_dickens_he_she_new)
ngrams_dickens_he_she_new %>%
group_by(ngrams) %>%
summarise(freq = sum(freq)) %>%
arrange(desc(freq))-> ngrams_dickens_he_she_new_count
#---------------------------
#Plotting the verbs:
hc_he_she_new <- highchart() %>%
hc_add_series(name = "frequency of verbs",data = ngrams_dickens_he_she_new_count[1:30,],
type = "column",
mapping = hcaes(x = ngrams, y = freq),
color = "blue") %>%
hc_xAxis(title = list(text = "Name of the Verbs"),
categories = ngrams_dickens_he_she_new_count$ngrams) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text ="Frequency of verbs in Charles Dickens works")
hc_he_she_newQuestion 8:
Using the ngram pacakge we will first find the 1-gram and bigram of the chapters. We found the chapters by spliting the book in places that have CHAPTER. We removed the contents page. We plotted for the Oliver Twist book because there will be many many plots ( more than 140 plots) for all of the books. But we will find the n-gram distribution for all the books. We could also use chi-square test but these graphs give us efficient information to.
#HW8-Q8:
d = list()
c = list()
#---------------------------
#Collectiing the book and cleaning it:
Oliver_Twist = gutenberg_download(730)
Oliver_Twist[[2]][1:117] <- " " #removing contents page
Oliver_Twist_new = Oliver_Twist %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]","")
chapters =str_split(Oliver_Twist_new, "CHAPTER")
#---------------------------
# Extracting the 1-grams for every chapter:
r_2 =length(chapters[[2]])
ngram_dickens = list()
index = list()
for (i in 2:r_2)
{
k = chapters[[2]][i] %>%
str_to_lower() %>%
str_split(pattern = "\\s") %>%
unlist() %>%
table() %>%
as.data.frame(stringsAsFactors = F)
colnames(k) = c("word","count")
k = k %>%
filter(!str_to_lower(word) %in% stop_words$word) %>%
filter(str_length(word)>1) %>%
filter(!str_detect(word,"\\d")) %>%
arrange(desc(count)) %>%
mutate(proper = !word %in% str_to_lower(word))
hc_chapter <- highchart() %>%
hc_add_series(name = "frequency of single words",data = k[1:50,],
type = "column",
mapping = hcaes(x = word, y = count),
color = "blue") %>%
hc_xAxis(title = list(text = "Name of the Words"),
categories = k$word) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text =sprintf("Frequency of words in Oliver Twist, Chapter %s"
,i))
d[[i]] = hc_chapter
}
#---------------------------
#Extracting 2-grams for every chapter:
ngram_dickens = list()
r_2 =length(chapters[[2]])
index = list()
for (i in 1:r_2)
{
b = 0
ngram_dickens = chapters[[2]][i] %>%
str_to_lower()
ngram_dickens= get.phrasetable(ngram(ngram_dickens,n=2))
ngram_dickens_dataframe_2= as.data.frame(ngram_dickens)
hc_chapter_2 <- highchart() %>%
hc_add_series(name = "frequency of bigrams",data = ngram_dickens_dataframe_2[1:50,],
type = "column",
mapping = hcaes(x = ngrams, y = freq),
color = "blue") %>%
hc_xAxis(title = list(text = "Name of the Words"),
categories = ngram_dickens_dataframe_2$ngrams) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text =sprintf("Frequency of bigram in Oliver Twist, Chapter %s"
,i))
c[[i]] = hc_chapter_2
}
#---------------------------
htmltools::tagList(c)htmltools::tagList(d)n-gram distribution of charles dickens books. We will use the logarithm of frequencies
#------
#ngram of charles dickens books:
rows = nrow(books_list)
for (i in 1:rows )
{
book_charles =gutenberg_download(books_list[i,1])
book_charles = book_charles %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]","\\!!!!!!")
book_charles <- sapply(book_charles,tolower)
ng <-ngram(book_charles,n=1)
ngram_1 = get.phrasetable(ng)
check = str_extract(ngram_1$ngrams,"\\!")
indexes = which(check==c("!"))
ngram_1 = ngram_1[-indexes,]
ng <-ngram(book_charles,n=2)
ngram_2 = get.phrasetable(ng)
check = str_extract(ngram_2$ngrams,"\\!")
indexes = which(check==c("!"))
ngram_2 = ngram_2[-indexes,]
ng <-ngram(book_charles,n=3)
ngram_3 = get.phrasetable(ng)
check = str_extract(ngram_3$ngrams,"\\!")
indexes = which(check==c("!"))
ngram_3= ngram_3[-indexes,]
ngram_1$index<-seq(1:nrow(ngram_1))
ngram_2$index<-seq(1:nrow(ngram_2))
ngram_3$index<-seq(1:nrow(ngram_3))
uni<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_1)
bi<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_2)
tri<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_3)
fit<-cbind(coef(uni), coef(bi), coef(tri))
print(fit)
library(ggplot2)
o = qplot(log(ngram_1$index),
log(ngram_1$freq))+
geom_abline(slope = fit[2, 1],
intercept = fit[1, 1])+
labs(x="Log(n-gram index)", y="Log(Number of freq)",
title=sprintf("Double logarithmic plot of the Unigram occurrence distribution
of %s",books_list[i,2]))
print(o)
index<-c(seq(1:10000), sample(seq(1:nrow(ngram_2)), 1000))
w = qplot(log(ngram_2$index),
log(ngram_2$freq))+
geom_abline(slope = fit[2, 2],
intercept = fit[1, 2])+
labs(x="Log(n-gram index)", y="Log(Number of freq)", title=sprintf("Double logarithmic plot of the bigram occurrence distribution
of %s",books_list[i,2]))
print(w)
index<-c(seq(1:10000), sample(seq(1:nrow(ngram_3)), 1000))
c = qplot(log(ngram_3$index),
log(ngram_3$freq))+
geom_abline(slope = fit[2, 3],
intercept = fit[1, 3])+
labs(x="Log(n-gram index)", y="Log(Number of freq)", title=sprintf("Double logarithmic plot of the trigram occurrence distribution
of %s",books_list[i,2]))
print(c)
check<-head(ngram_1, n=50)
x = qplot(check$ngrams,
check$freq/sum(ngram_1$freq)) +
geom_bar(position = "dodge",
stat="identity")+coord_flip() +
scale_x_discrete(limits=check$ngrams[order(check$freq,
decreasing=F)])+labs(x="Unigram name",
y="Relative freq", title=sprintf("Unigram distribution of %s",
books_list[i,2]))
print(x)
check<-head(ngram_2, n=50)
z = qplot(check$ngrams,
check$freq/sum(ngram_2$freq)) +
geom_bar(position = "dodge",
stat="identity")+coord_flip() +
scale_x_discrete(limits=check$ngrams[order(check$freq,
decreasing=F)])+labs(x="Unigram name",
y="Relative freq", title=sprintf("Bigram distribution of %s",
books_list[i,2]))
print(z)
check<-head(ngram_3, n=50)
q = qplot(check$ngrams,
check$freq/sum(ngram_3$freq)) +
geom_bar(position = "dodge",
stat="identity")+coord_flip() +
scale_x_discrete(limits=check$ngrams[order(check$freq,
decreasing=F)])+labs(x="Unigram name",
y="Relative freq", title=sprintf("Trigram distribution of %s",
books_list[i,2]))
print(q)
}## [,1] [,2] [,3]
## (Intercept) 11.337486 6.7336969 3.0715170
## log(index) -1.224877 -0.6375162 -0.2862819
## [,1] [,2] [,3]
## (Intercept) 10.313163 5.9551627 2.396489
## log(index) -1.169316 -0.5919164 -0.235902
## [,1] [,2] [,3]
## (Intercept) 11.517407 6.9641867 3.0136661
## log(index) -1.242178 -0.6525349 -0.2768444
## [,1] [,2] [,3]
## (Intercept) 10.88178 6.4141556 2.627028
## log(index) -1.20301 -0.6166259 -0.248349
## [,1] [,2] [,3]
## (Intercept) 11.158596 6.6494959 2.7824117
## log(index) -1.222728 -0.6327971 -0.2601516
## [,1] [,2] [,3]
## (Intercept) 11.478739 7.0608187 3.1845216
## log(index) -1.237668 -0.6603593 -0.2912038
## [,1] [,2] [,3]
## (Intercept) 11.690652 7.1875756 3.2208653
## log(index) -1.258168 -0.6696099 -0.2931969
## [,1] [,2] [,3]
## (Intercept) 11.722728 7.4793938 3.6324878
## log(index) -1.276474 -0.7023451 -0.3320427
## [,1] [,2] [,3]
## (Intercept) 11.713911 7.282158 3.3609825
## log(index) -1.262171 -0.676684 -0.3039053
## [,1] [,2] [,3]
## (Intercept) 9.324327 5.3676629 2.0068388
## log(index) -1.076365 -0.5472623 -0.2030397
## [,1] [,2] [,3]
## (Intercept) 11.583040 7.171362 3.2721660
## log(index) -1.249158 -0.668076 -0.2969449
## [,1] [,2] [,3]
## (Intercept) 9.975313 5.8303242 2.267838
## log(index) -1.139437 -0.5835611 -0.224106
## [,1] [,2] [,3]
## (Intercept) 10.515060 6.5552689 2.9856149
## log(index) -1.187739 -0.6427636 -0.2861696
## [,1] [,2] [,3]
## (Intercept) 11.392205 7.0771495 3.1872599
## log(index) -1.230649 -0.6636513 -0.2919391
## [,1] [,2] [,3]
## (Intercept) 9.098942 5.1228686 1.8257404
## log(index) -1.058781 -0.5280096 -0.1872684
Question 9:
We will do the same thing for Jane Eyre and Bronte’s works.
#HW8-q9:
#---------------------------
#Collectiing the book and cleaning it:
Jane_Eyre = gutenberg_download(1260)
Jane_Eyre[[2]][1:117] <- " " #removing contents page
Jane_Eyre_new = Jane_Eyre %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]","")
chapters =str_split(Jane_Eyre_new, "CHAPTER")
#---------------------------
# Extracting the 1-grams for every chapter:
r_2 =length(chapters[[2]])
ngram_dickens = list()
index = list()
for (i in 2:r_2)
{
k = chapters[[2]][i] %>%
str_to_lower() %>%
str_split(pattern = "\\s") %>%
unlist() %>%
table() %>%
as.data.frame(stringsAsFactors = F)
colnames(k) = c("word","count")
k = k %>%
filter(!str_to_lower(word) %in% stop_words$word) %>%
filter(str_length(word)>1) %>%
filter(!str_detect(word,"\\d")) %>%
arrange(desc(count)) %>%
mutate(proper = !word %in% str_to_lower(word))
hc_chapter <- highchart() %>%
hc_add_series(name = "frequency of single words",data = k[1:50,],
type = "column",
mapping = hcaes(x = word, y = count),
color = "blue") %>%
hc_xAxis(title = list(text = "Name of the Words"),
categories = k$word) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text =sprintf("Frequency of words in Jane Eyre, Chapter %s"
,i))
d[[i]] = hc_chapter
}
#---------------------------
#Extracting 2-grams for every chapter:
ngram_dickens = list()
r_2 =length(chapters[[2]])
index = list()
for (i in 1:r_2)
{
b = 0
ngram_dickens = chapters[[2]][i] %>%
str_to_lower()
ngram_dickens= get.phrasetable(ngram(ngram_dickens,n=2))
ngram_dickens_dataframe_2= as.data.frame(ngram_dickens)
hc_chapter_2 <- highchart() %>%
hc_add_series(name = "frequency of bigrams",data = ngram_dickens_dataframe_2[1:50,],
type = "column",
mapping = hcaes(x = ngrams, y = freq),
color = "blue") %>%
hc_xAxis(title = list(text = "Name of the Words"),
categories = ngram_dickens_dataframe_2$ngrams) %>%
hc_yAxis(title = list(text = "Frequency")) %>%
hc_title(text =sprintf("Frequency of bigram in Jane Eyre, Chapter %s"
,i))
c[[i]] = hc_chapter_2
}
#---------------------------
htmltools::tagList(c)htmltools::tagList(d)#------
#ngram of charles Bronte books:
library(ngram)
book_bronte = gutenberg_works(str_detect(author, "Bronte"))
book_bronte %>% select(gutenberg_id,title) -> book_bronte
for (i in 1:3 )
{
book_bronte_d =gutenberg_download(book_bronte[i,1])
book_bronte_d = book_bronte_d %>%
str_replace_all("\"","") %>%
str_replace_all("[[:punct:]]","\\!!!!!!")
book_bronte_d <- sapply(book_bronte_d,tolower)
ng <-ngram(book_bronte_d,n=1)
ngram_1 = get.phrasetable(ng)
check = str_extract(ngram_1$ngrams,"\\!")
indexes = which(check==c("!"))
ngram_1 = ngram_1[-indexes,]
ng <-ngram(book_bronte_d,n=2)
ngram_2 = get.phrasetable(ng)
check = str_extract(ngram_2$ngrams,"\\!")
indexes = which(check==c("!"))
ngram_2 = ngram_2[-indexes,]
ng <-ngram(book_bronte_d,n=3)
ngram_3 = get.phrasetable(ng)
check = str_extract(ngram_3$ngrams,"\\!")
indexes = which(check==c("!"))
ngram_3= ngram_3[-indexes,]
ngram_1$index<-seq(1:nrow(ngram_1))
ngram_2$index<-seq(1:nrow(ngram_2))
ngram_3$index<-seq(1:nrow(ngram_3))
uni<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_1)
bi<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_2)
tri<-glm(log(freq) ~ log(index), family="gaussian", data=ngram_3)
fit<-cbind(coef(uni), coef(bi), coef(tri))
print(fit)
library(ggplot2)
w = qplot(log(ngram_1$index),
log(ngram_1$freq))+
geom_abline(slope = fit[2, 1],
intercept = fit[1, 1])+
labs(x="Log(n-gram index)", y="Log(Number of freq)",
title=sprintf("Double logarithmic plot of the Unigram occurrence distribution
of %s",book_bronte[i,2]))
print(w)
z = qplot(log(ngram_2$index),
log(ngram_2$freq))+
geom_abline(slope = fit[2, 2],
intercept = fit[1, 2])+
labs(x="Log(n-gram index)", y="Log(Number of freq)", title=sprintf("Double logarithmic plot of the bigram occurrence distribution
of %s",book_bronte[i,2]))
print(z)
x = qplot(log(ngram_3$index),
log(ngram_3$freq))+
geom_abline(slope = fit[2, 3],
intercept = fit[1, 3])+
labs(x="Log(n-gram index)", y="Log(Number of freq)", title=sprintf("Double logarithmic plot of the trigram occurrence distribution
of %s",book_bronte[i,2]))
print(x
)
check<-head(ngram_1, n=50)
c= qplot(check$ngrams,
check$freq/sum(ngram_1$freq)) +
geom_bar(position = "dodge",
stat="identity")+coord_flip() +
scale_x_discrete(limits=check$ngrams[order(check$freq,
decreasing=F)])+labs(x="Unigram name",
y="Relative freq", title=sprintf("Unigram distribution of %s",
book_bronte[i,2]))
print(c)
check<-head(ngram_2, n=50)
v = qplot(check$ngrams,
check$freq/sum(ngram_2$freq)) +
geom_bar(position = "dodge",
stat="identity")+coord_flip() +
scale_x_discrete(limits=check$ngrams[order(check$freq,
decreasing=F)])+labs(x="Unigram name",
y="Relative freq", title=sprintf("Bigram distribution of %s",
book_bronte[i,2]))
print(v)
check<-head(ngram_3, n=50)
m= qplot(check$ngrams,
check$freq/sum(ngram_3$freq)) +
geom_bar(position = "dodge",
stat="identity")+coord_flip() +
scale_x_discrete(limits=check$ngrams[order(check$freq,
decreasing=F)])+labs(x="Unigram name",
y="Relative freq", title=sprintf("Trigram distribution of %s",
book_bronte[i,2]))
print(m)
}## [,1] [,2] [,3]
## (Intercept) 7.3770649 3.2793818 0.8569237
## log(index) -0.9755308 -0.3884393 -0.1031736
## [,1] [,2] [,3]
## (Intercept) 5.517735 2.2969672 0.7906645
## log(index) -0.975414 -0.3663235 -0.1290325
## [,1] [,2] [,3]
## (Intercept) 5.2470129 2.0516256 0.6649229
## log(index) -0.9273681 -0.3333573 -0.1116311
## Question 10: We can use the bigram,unigram and trigram of the works of Dickens and Bronte and use them as an independent variable. Using these values in different columns we can make a logistic model, but it’s been a very very long home-work and i’m way too tired for modeling this. So i may end this question with the given description of how we can make the logistic model.